Method and apparatus and program storage device adapted for automatic qualitative and quantitative risk assessment based on technical wellbore design and earth properties

ABSTRACT

A Software System, known as an Automatic Well Planning Risk Assessment Software System, is adapted to determine and display risk information in response to a plurality of input data by: receiving the plurality of input data, the input data including a plurality of input data calculation results; comparing each calculation result of the plurality of input data calculation results with each logical expression of a plurality of logical expressions; ranking by the logical expression the calculation result; and generating a plurality of ranked risk values in response thereto, each of the plurality of ranked risk values representing an input data calculation result that has been ranked by the logical expression as either a high risk or a medium risk or a low risk; generating the risk information in response to the plurality of ranked risk values; and displaying the risk information.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to application Ser. No. 10/802,507filed Mar.17, 2004, now U.S. Pat. No. 7,258,175; application Ser. No.10/802,545filed Mar. 17, 2004, now U.S. Pat. No. 7,546,884 ; applicationSer. No. 10/802,613filed Mar. 17, 2004; application Ser. No.10/802,622filed Mar. 17, 2004, now U.S. Pat. No. 7,539,625 andapplication Ser. No. 11/053,575, filed Feb. 8, 2005, now U.S. Pat. No.7,548,873.

BACKGROUND OF THE INVENTION

The subject matter of the present invention relates to a software systemadapted to be stored in a computer system, such as a personal computer,for providing a qualitative and quantitative risk assessment based ontechnical wellbore design and Earth properties.

Minimizing wellbore costs and associated risks requires wellboreconstruction planning techniques that account for the interdependenciesinvolved in the wellbore design. The inherent difficulty is that mostdesign processes and systems exist as independent tools used forindividual tasks by the various disciplines involved in the planningprocess. In an environment where increasingly difficult wells of highervalue are being drilled with fewer resources, there is now, more thanever, a need for a rapid well-planning, cost, and risk assessment tool.

This specification discloses a software system representing an automatedprocess adapted for integrating both a wellbore construction planningworkflow and accounting for process interdependencies. The automatedprocess is based on a drilling simulator, the process representing ahighly interactive process which is encompassed in a software systemthat: (1) allows well construction practices to be tightly linked togeological and geomechanical models, (2) enables asset teams to planrealistic well trajectories by automatically generating cost estimateswith a risk assessment, thereby allowing quick screening and economicevaluation of prospects, (3) enables asset teams to quantify the valueof additional information by providing insight into the business impactof project uncertainties, (4) reduces the time required for drillingengineers to assess risks and create probabilistic time and costestimates faithful to an engineered well design, (5) permits drillingengineers to immediately assess the business impact and associated risksof applying new technologies, new procedures, or different approaches toa well design. Discussion of these points illustrate the application ofthe workflow and verify the value, speed, and accuracy of thisintegrated well planning and decision-support tool.

Identifying the risks associated with drilling a well is probably themost subjective process in well planning today. This is based on aperson recognizing part of a technical well design that is out of placerelative to the earth properties or mechanical equipment to be used todrill the well. The identification of any risks is brought about byintegrating all of the well, earth, and equipment information in themind of a person and mentally sifting through all of the information,mapping the interdependencies, and based solely on personal experienceextracting which parts of the project pose what potential risks to theoverall success of that project. This is tremendously sensitive to humanbias, the individual's ability to remember and integrate all of the datain their mind, and the individuals experience to enable them torecognize the conditions that trigger each drilling risk. Most peopleare not equipped to do this and those that do are very inconsistentunless strict process and checklists are followed. There are somedrilling risk software systems in existence today, but they all requirethe same human process to identify and assess the likelihood of eachindividual risks and the consequences. They are simply a computer systemfor manually recording the results of the risk identification process.

The Risk Assessment sub-task associated with the ‘Automatic WellPlanning Software System’ of the present invention is a system that willautomatically assess risks associated with the technical well designdecisions in relation to the earth's geology and geomechanicalproperties and in relation to the mechanical limitations of theequipment specified or recommended for use.

SUMMARY OF THE INVENTION

One aspect of the present invention involves a method of determining anddisplaying risk information in response to a plurality of input data,comprising the steps of: receiving the plurality of input data, theinput data including a plurality of input data calculation results;comparing each calculation result of the plurality of input datacalculation results with each logical expression of a plurality oflogical expressions, ranking by the logical expression the calculationresult, and generating a plurality of ranked risk values in responsethereto, each of the plurality of ranked risk values representing aninput data calculation result that has been ranked by the logicalexpression as either a high risk or a medium risk or a low risk;generating the risk information in response to the plurality of rankedrisk values; and displaying the risk information.

Another aspect of the present invention involves a program storagedevice readable by a machine tangibly embodying a program ofinstructions executable by the machine to perform method steps fordetermining and displaying risk information in response to a pluralityof input data, the method steps comprising: receiving the plurality ofinput data, the input data including a plurality of input datacalculation results; comparing each calculation result of the pluralityof input data calculation results with each logical expression of aplurality of logical expressions, ranking by the logical expression thecalculation result, and generating a plurality of ranked risk values inresponse thereto, each of the plurality of ranked risk valuesrepresenting an input data calculation result that has been ranked bythe logical expression as either a high risk or a medium risk or a lowrisk; generating the risk information in response to the plurality ofranked risk values; and displaying the risk information.

Another aspect of the present invention involves a system adapted fordetermining and displaying risk information in response to a pluralityof input data, comprising: apparatus adapted for receiving the pluralityof input data, the input data including a plurality of input datacalculation results; apparatus adapted for comparing each calculationresult of the plurality of input data calculation results with eachlogical expression of a plurality of logical expressions, ranking, bythe logical expression, the calculation result, and generating aplurality of ranked risk values in response thereto, each of theplurality of ranked risk values representing an input data calculationresult that has been ranked by the logical expression as either a highrisk or a medium risk or a low risk; apparatus adapted for generatingthe risk information in response to the plurality of ranked risk values;and apparatus adapted for displaying the risk information.

Further scope of applicability of the present invention will becomeapparent from the detailed description presented hereinafter. It shouldbe understood, however, that the detailed description and the specificexamples, while representing a preferred embodiment of the presentinvention, are given by way of illustration only, since various changesand modifications within the spirit and scope of the invention willbecome obvious to one skilled in the art from a reading of the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

A full understanding of the present invention will be obtained from thedetailed description of the preferred embodiment presented hereinbelow,and the accompanying drawings, which are given by way of illustrationonly and are not intended to be limitative of the present invention, andwherein:

FIG. 1 illustrates a software architecture schematic indicating amodular nature to support custom workflows;

FIG. 2 including FIGS. 2A, 2B, 2C, and 2D illustrates a typical taskview consisting of workflow, help and data canvases;

FIG. 3 including FIGS. 3A, 3B, 3C, and 3D illustrates wellborestability, mud weights, and casing points;

FIG. 4 including FIGS. 4A, 4B, 4C, and 4D illustrates risk assessment;

FIG. 5 including FIGS. 5A, 5B, 5C, and 5D illustrates a Monte Carlo timeand cost distribution;

FIG. 6 including FIGS. 6A, 6B, 6C, and 6D illustrates a probabilistictime and cost vs. depth;

FIG. 7 including FIGS. 7A, 7B, 7C, and 7D illustrates a summary montage;

FIG. 8 illustrates a workflow in an ‘Automatic Well Planning SoftwareSystem’ of the present invention;

FIG. 9A illustrates a computer system storing an Automatic Well PlanningRisk Assessment Software of the present invention;

FIG. 9B illustrates a display as shown on a Recorder or Display deviceof the Computer System of FIG. 9A;

FIG. 10 illustrates a detailed construction of the Automatic WellPlanning Risk Assessment Software stored in the Computer System of FIG.9A; and

FIG. 11 illustrates a block diagram which is used during a functionaldescription of the operation of the present invention.

DETAILED DESCRIPTION

An ‘Automatic Well Planning Software System’ in accordance with thepresent invention is disclosed in this specification. The ‘AutomaticWell Planning Software System’ of the present invention is a “smart”tool for rapid creation of a detailed drilling operational plan thatprovides economics and risk analysis. The user inputs trajectory andearth properties parameters; the system uses this data and variouscatalogs to calculate and deliver an optimum well design therebygenerating a plurality of outputs, such as drill string design, casingseats, mud weights, bit selection and use, hydraulics, and the otheressential factors for the drilling task. System tasks are arranged in asingle workflow in which the output of one task is included as input tothe next. The user can modify most outputs, which permits fine-tuning ofthe input values for the next task. The ‘Automatic Well PlanningSoftware System’ has two primary user groups: (1) Geoscientist: Workswith trajectory and earth properties data; the ‘Automatic Well PlanningSoftware System’ provides the necessary drilling engineeringcalculations; this allows the user to scope drilling candidates rapidlyin terms of time, costs, and risks; and (2) Drilling engineer: Workswith wellbore geometry and drilling parameter outputs to achieve optimumactivity plan and risk assessment; Geoscientists typically provide thetrajectory and earth properties data. The scenario, which consists ofthe entire process and its output, can be exported for sharing withother users for peer review or as a communication tool to facilitateproject management between office and field. Variations on a scenariocan be created for use in business decisions. The ‘Automatic WellPlanning Software System’ can also be used as a training tool forgeoscientists and drilling engineers.

The ‘Automatic Well Planning Software System’ of the present inventionwill enable the entire well construction workflow to be run throughquickly. In addition, the ‘Automatic Well Planning Software System’ canultimately be updated and re-run in a time-frame that supportsoperational decision making. The entire replanning process must be fastenough to allow users to rapidly iterate to refine well plans through aseries of what-if scenarios.

The decision support algorithms provided by the ‘Automatic Well PlanningSoftware System’ disclosed in this specification would link geologicaland geomechanical data with the drilling process (casing points, casingdesign, cement, mud, bits, hydraulics, etc) to produce estimates and abreakdown of the well time, costs, and risks. This will allowinterpretation variations, changes, and updates of the Earth Model to bequickly propogated through the well planning process.

The software associated with the aforementioned ‘Automatic Well PlanningSoftware System’ accelerates the prospect selection, screening, ranking,and well construction workflows. The target audiences are two fold:those who generate drilling prospects, and those who plan and drillthose prospects. More specifically, the target audiences include: AssetManagers, Asset Teams (Geologists, Geophysicists, Reservoir Engineers,and Production Engineers), Drilling Managers, and Drilling Engineers.

Asset Teams will use the software associated with the ‘Automatic WellPlanning Software System’ as a scoping tool for cost estimates, andassessing mechanical feasibility, so that target selection and wellplacement decisions can be made more knowledgeably, and moreefficiently. This process will encourage improved subsurface evaluationand provide a better appreciation of risk and target accessibility.Since the system can be configured to adhere to company or local designstandards, guidelines, and operational practices, users will beconfident that well plans are technically sound.

Drilling Engineers will use the software associated with the ‘AutomaticWell Planning Software System’ disclosed in this specification for rapidscenario planning, risk identification, and well plan optimization. Itwill also be used for training, in planning centers, universities, andfor looking at the drilling of specific wells, electronically drillingthe well, scenario modeling and ‘what-if’ exercises, prediction anddiagnosis of events, post-drilling review and knowledge transfer.

The software associated with the ‘Automatic Well Planning SoftwareSystem’ will enable specialists and vendors to demonstratedifferentiation amongst new or competing technologies. It will allowoperators to quantify the risk and business impact of the application ofthese new technologies or procedures.

Therefore, the ‘Automatic Well Planning Software System’ disclosed inthis specification, in accordance with the present invention, will: (1)dramatically improve the efficiency of the well planning and drillingprocesses by incorporating all available data and well engineeringprocesses in a single predictive well construction model, (2) integratepredictive models and analytical solutions for wellbore stability, mudweights & casing seat selection, tubular & hole size selection, tubulardesign, cementing, drilling fluids, bit selection, rate of penetration,BHA design, drillstring design, hydraulics, risk identification,operations planning, and probabilistic time and cost estimation, allwithin the framework of a mechanical earth model, (3) easily andinteractively manipulate variables and intermediate results withinindividual scenarios to produce sensitivity analyses. As a result, whenthe ‘Automatic Well Planning Software System’ of the present inventionis utilized, the following results will be achieved: (1) more accurateresults, (2) more effective use of engineering resources, (3) increasedawareness, (4) reduced risks while drilling, (5) decreased well costs,and (6) a standard methodology or process for optimization throughiteration in planning and execution. As a result, during theimplementation of the ‘Automatic Well Planning Software System’ of thepresent invention, the emphasis was placed on architecture andusability.

In connection with the implementation of the ‘Automatic Well PlanningSoftware System’ of the present invention, the software developmenteffort was driven by the requirements of a flexible architecture whichmust permit the integration of existing algorithms and technologies withcommercial-off-the-shelf (COTS) tools for data visualization.Additionally, the workflow demanded that the product be portable,lightweight and fast, and require a very small learning curve for users.Another key requirement was the ability to customize the workflow andconfiguration based on proposed usage, user profile and equipmentavailability.

The software associated with the ‘Automatic Well Planning SoftwareSystem’ was developed using the ‘Ocean’ framework owned by SchlumbergerTechnology Corporation of Houston, Tex. This framework uses Microsoft's.NET technologies to provide a software development platform whichallows for easy integration of COTS software tools with a flexiblearchitecture that was specifically designed to support custom workflowsbased on existing drilling algorithms and technologies.

Referring to FIG. 1, a software architecture schematic is illustratedindicating the ‘modular nature’ for supporting custom workflows. FIG. 1schematically shows the modular architecture that was developed tosupport custom workflows. This provides the ability to configure theapplication based on the desired usage. For a quick estimation of thetime, cost and risk associated with the well, a workflow consisting oflookup tables and simple algorithms can be selected. For a more detailedanalysis, complex algorithms can be included in the workflow.

In addition to customizing the workflow, the software associated withthe ‘Automatic Well Planning Software System’ of the present inventionwas designed to use user-specified equipment catalogs for its analysis.This ensures that any results produced by the software are always basedon local best practices and available equipment at the project site.From a usability perspective, application user interfaces were designedto allow the user to navigate through the workflow with ease.

Referring to FIG. 2, a typical task view consisting of workflow, helpand data canvases is illustrated. FIG. 2 shows a typical task view withits associated user canvases. A typical task view consists of a workflowtask bar, a dynamically updating help canvas, and a combination of datacanvases based on COTS tools like log graphics, Data Grids, WellboreSchematic and charting tools. In any task, the user has the option tomodify data through any of the canvases; the application thenautomatically synchronizes the data in the other canvases based on theseuser modifications.

The modular nature of the software architecture associated with the‘Automatic Well Planning Software System’ of the present invention alsoallows the setting-up of a non-graphical workflow, which is key toimplementing advanced functionality, such as batch processing of anentire field, and sensitivity analysis based on key parameters, etc.

Basic information for a scenario, typical of well header information forthe well and wellsite, is captured in the first task. The trajectory(measured depth, inclination, and azimuth) is loaded and the otherdirectional parameters like true vertical depth and dogleg severity arecalculated automatically and graphically presented to the user.

The ‘Automatic Well Planning Software System’ disclosed in thisspecification, in accordance with the present invention, requires theloading of either geomechanical earth properties extracted from an earthmodel, or, at a minimum, pore pressure, fracture gradient, andunconfined compressive strength. From this input data, the ‘AutomaticWell Planning Software System’ automatically selects the mostappropriate rig and associated properties, costs, and mechanicalcapabilities. The rig properties include parameters like derrick ratingto evaluate risks when running heavy casing strings, pumpcharacteristics for the hydraulics, size of the BOP, which influencesthe sizes of the casings, and very importantly the daily rig rate andspread rate. The user can select a different rig than what the‘Automatic Well Planning Software System’ proposed and can modify any ofthe technical specifications suggested by the software.

Other wellbore stability algorithms (which are offered by SchlumbergerTechnology Corporation, or Houston, Tex.) calculate the predicted shearfailure and the fracture pressure as a function of depth and displaythese values with the pore pressure. The ‘Automatic Well PlanningSoftware System’ then proposes automatically the casing seats andmaximum mud weight per hole section using customizable logic and rules.The rules include safety margins to the pore pressure and fracturegradient, minimum and maximum lengths for hole sections and limits formaximum overbalance of the drilling fluid to the pore pressure before asetting an additional casing point. The ‘Automatic Well PlanningSoftware System’ evaluates the casing seat selection from top-to-bottomand from bottom-to-top and determines the most economic variant. Theuser can change, insert, or delete casing points at any time, which willreflect in the risk, time, and cost for the well.

Referring to FIG. 3, a display showing wellbore stability, mud weights,and casing points is illustrated.

The wellbore sizes are driven primarily by the production tubing size.The preceding casing and hole sizes are determined using clearancefactors. The wellbore sizes can be restricted by additional constraints,such as logging requirements or platform slot size. Casing weights,grades, and connection types are automatically calculated usingtraditional biaxial design algorithms and simple load cases for burst,collapse and tension. The most cost effective solution is chosen whenmultiple suitable pipes are found in the extensive tubular catalog.Non-compliance with the minimum required design factors are highlightedto the user, pointing out that a manual change of the proposed designmay be in order. The ‘Automatic Well Planning Software System’ allowsfull strings to be replaced with liners, in which case, the lineroverlap and hanger cost are automatically suggested while all stringsare redesigned as necessary to account for changes in load cases. Thecement slurries and placement are automatically proposed by the‘Automatic Well Planning Software System’. The lead and tail cementtops, volumes, and densities are suggested. The cementing hydrostaticpressures are validated against fracture pressures, while allowing theuser to modify the slurry interval tops, lengths, and densities. Thecost is derived from the volume of the cement job and length of timerequired to place the cement.

The ‘Automatic Well Planning Software System’ proposes the properdrilling fluid type including rheology properties that are required forhydraulic calculations. A sophisticated scoring system ranks theappropriate fluid systems, based on operating environment, dischargelegislation, temperature, fluid density, wellbore stability, wellborefriction and cost. The system is proposing not more than 3 differentfluid systems for a well, although the user can easily override theproposed fluid systems.

A new and novel algorithm used by the ‘Automatic Well Planning SoftwareSystem’ selects appropriate bit types that are best suited to theanticipated rock strengths, hole sizes, and drilled intervals. For eachbit candidate, the footage and bit life is determined by comparing thework required to drill the rock interval with the statistical workpotential for that bit. The most economic bit is selected from allcandidates by evaluating the cost per foot which takes into account therig rate, bit cost, tripping time and drilling performance (ROP).Drilling parameters like string surface revolutions and weight on bitare proposed based on statistical or historical data.

In the ‘Automatic Well Planning Software System’, the bottom holeassembly (BHA) and drillstring is designed based on the required maximumweight on bit, inclination, directional trajectory and formationevaluation requirements in the hole section. The well trajectoryinfluences the relative weight distribution between drill collars andheavy weight drill pipe. The BHA components are automatically selectedbased on the hole size, the internal diameter of the preceding casings,and bending stress ratios are calculated for each component sizetransition. Final kick tolerances for each hole section are alsocalculated as part of the risk analysis.

The minimum flow rate for hole cleaning is calculated using Luo's² andMoore's³ criteria considering the wellbore geometry, BHA configuration,fluid density and rheology, rock density, and ROP. The bit nozzles totalflow area (TFA) are sized to maximize the standpipe pressure within theliner operating pressure envelopes. Pump liner sizes are selected basedon the flow requirements for hole cleaning and corresponding circulatingpressures. The Power Law rheology model is used to calculate thepressure drops through the circulating system, including the equivalentcirculating density (ECD).

Referring to FIG. 4, a display showing ‘Risk Assessment’ is illustrated.

In FIG. 4, in the ‘Automatic Well Planning Software System’, drillingevent ‘risks’ are quantified in a total of 54 risk categories of whichthe user can customize the risk thresholds. The risk categories areplotted as a function of depth and color coded to aid a quick visualinterpretation of potential trouble spots. Further risk assessment isachieved by grouping these categories in the following categories:‘gains’, ‘losses’, ‘stuck pipe’, and ‘mechanical problems’. The totalrisk log curve can be displayed along the trajectory to correlatedrilling risks with geological markers. Additional risk analysis viewsdisplay the “actual risk” as a portion of the “potential risk” for eachdesign task.

In the ‘Automatic Well Planning Software System’, a detailed operationalactivity plan is automatically assembled from customizable templates.The duration for each activity is calculated based on the engineeredresults of the previous tasks and Non-Productive Time (NPT) can beincluded. The activity plan specifies a range (minimum, average, andmaximum) of time and cost for each activity and lists the operationssequentially as a function of depth and hole section. This informationis graphically presented in the time vs depth and cost vs depth graphs.

Referring to FIG. 5, a display showing Monte Carlo time and costdistributions is illustrated. In FIG. 5, the ‘Automatic Well PlanningSoftware System’ uses Monte Carlo simulation to reconcile all of therange of time and cost data to produce probabilistic time and costdistributions.

Referring to FIG. 6, a display showing Probabilistic time and cost vs.depth is illustrated. In FIG. 6, this probabilistic analysis, used bythe ‘Automatic Well Planning Software System’ of the present invention,allows quantifying the P10, P50 and P90 probabilities for time and cost.

Referring to FIG. 7, a display showing a summary montage is illustrated.In FIG. 7, a comprehensive summary report and a montage display,utilized by the ‘Automatic Well Planning Software System’ of the presentinvention, can be printed or plotted in large scale and are alsoavailable as a standard result output.

Using its expert system and logic, the ‘Automatic Well Planning SoftwareSystem’ disclosed in this specification, in accordance with the presentinvention, automatically proposes sound technical solutions and providesa smooth path through the well planning workflow. Graphical interactionwith the results of each task allows the user to efficiently fine-tunethe results. In just minutes, asset teams, geoscientists, and drillingengineers can evaluate drilling projects and economics usingprobabilistic cost estimates based on solid engineering fundamentalsinstead of traditional, less rigorous estimation methods. The testingprogram combined with feedback received from other users of the programduring the development of the software package made it possible to drawthe following conclusions: (1) The ‘Automatic Well Planning SoftwareSystem’ can be installed and used by inexperienced users with a minimumamount of training and by referencing the documentation provided, (2)The need for good earth property data enhances the link to geologicaland geomechanical models and encourages improved subsurfaceinterpretation; it can also be used to quantify the value of acquiringadditional information to reduce uncertainty, (3) With a minimum amountof input data, the ‘Automatic Well Planning Software System’ can createreasonable probabilistic time and cost estimates faithful to anengineered well design; based on the field test results, if the numberof casing points and rig rates are accurate, the results will be within20% of a fully engineered well design and AFE, (4) With additionalcustomization and localization, predicted results compare to within 10%of a fully engineered well design AFE, (5) Once the ‘Automatic WellPlanning Software System’ has been localized, the ability to quickly runnew scenarios and assess the business impact and associated risks ofapplying new technologies, procedures or approaches to well designs isreadily possible, (6) The speed of the ‘Automatic Well Planning SoftwareSystem’ allows quick iteration and refinement of well plans and creationof different ‘what if’ scenarios for sensitivity analysis, (7) The‘Automatic Well Planning Software System’ provides consistent andtransparent well cost estimates to a process that has historically beenarbitrary, inconsistent, and opaque; streamlining the workflow andeliminating human bias provides drilling staff the confidence todelegate and empower non-drilling staff to do their own scopingestimates, (8) The ‘Automatic Well Planning Software System’ providesunique understanding of drilling risk and uncertainty enabling morerealistic economic modeling and improved decision making, (9) The riskassessment accurately identifies the type and location of risk in thewellbore enabling drilling engineers to focus their detailed engineeringefforts most effectively, (10) It was possible to integrate and automatethe well construction planning workflow based on an earth model andproduce technically sound usable results, (11) The project was able toextensively use COTS technology to accelerate development of thesoftware, and (12) The well engineering workflow interdependencies wereable to be mapped and managed by the software.

The following nomenclature was used in this specification:

-   RT=Real-Time, usually used in-the context of real-time data (while    drilling).-   G&G=Geological and Geophysical-   SEM=Shared Earth Model-   MEM=Mechanical Earth Model-   NPT=Non Productive Time, when operations are not planned, or due to    operational difficulties, the progress of the well has be delayed,    also often referred to as Trouble Time.-   NOT=Non Optimum Time, when operations take longer than they should    for various reasons.-   WOB=Weight on bit-   ROP=Rate of penetration-   RPM=Revolutions per minute-   BHA=Bottom hole assembly-   SMR=Software Modification Request-   BOD=Basis of Design, document specifying the requirements for a well    to be drilled.-   AFE=Authorization for Expenditure

REFERENCES

-   (1) Booth, J., Bradford, I. D. R., Cook, J. M., Dowell, J. D.,    Ritchie, G., Tuddenham, I.: ‘Meeting Future Drilling Planning and    Decision Support Requirements: A New Drilling Simulator’, IADC/SPE    67816 presented at the 2001 IADC/SPE Drilling Conference, Amsterdam,    The Netherlands, February 27-March 1.-   (2) Luo, Y., Bern, P. A. and Chambers, B. D.: ‘Flow-Rate Predictions    for Cleaning Deviated Wells’, paper IADC/SPE 23884 presented at the    1992 IADC/SPE Drilling Conference, New Orleans, La., Feb. 18-21.-   (3) Moore and Chien theory is published in ‘Applied Drilling    Engineering’, Bourgoyne, A. T., Jr, et al., SPE Textbook Series    Vol2.

A functional specification associated with the overall ‘Automatic WellPlanning Software System’ of the present invention (termed a ‘use case’)will be set forth in the following paragraphs. This functionalspecification relates to the overall ‘Automatic Well Planning SoftwareSystem’.

The following defines information that pertains to this particular ‘usecase’. Each piece of information is important in understanding thepurpose behind the ‘use Case’.

Goal In Context: Describe the full workflow for the low level userScope: N/A Level: Low Level Pre-Condition: Geological targetspre-defined Success End Condition: Probability based time estimate withcost and risk Failed End Condition: Failure in calculations due toassumptions or if distribution of results is too large Primary Actor:Well Engineer Trigger Event: N/A

Main Success Scenario—This Scenario describes the steps that are takenfrom trigger event to goal completion when everything works withoutfailure. It also describes any required cleanup that is done after thegoal has been reached. The steps are listed below:

-   1. User opens program, and system prompts user whether to open an    old file or create a new one. User creates new model and system    prompts user for well information (well name, field, country,    coordinates). System prompts user to insert earth model. Window with    different options appears and user selects data level. Secondary    window appears where file is loaded or data inserted manually.    System displays 3D view of earth model with key horizons, targets,    anti-targets, markers, seismic, etc.-   2. System prompts user for a well trajectory. The user either loads    from a file or creates one in Caviar for Swordfish. System generates    3D view of trajectory in the earth model and 2D views, both plan and    vertical section. User prompted to verify trajectory and modify if    needed via direct interaction with 3D window.-   3. The system will extract mechanical earth properties (PP, FG, WBS,    lithology, density, strength, min/max horizontal stress, etc.) for    every point along the trajectory and store it. These properties will    either come from a populated mechanical earth model, from    interpreted logs applied to this trajectory, or manually entered.-   4. The system will prompt the user for the rig constraints. Rig    specification options will be offered and the user will choose    either the type of rig and basic configurations or insert data    manually for a specific drilling unit.-   5. The system will prompt the user to enter pore pressure data, if    applicable, otherwise taken from the mechanical earth model    previously inserted and a MW window will be generated using PP, FG,    and WBS curves. The MW window will be displayed and allow    interactive modification.-   6. The system will automatically divide the well into hole/casing    sections based on kick tolerance and trajectory sections and then    propose a mud weight schedule. These will be displayed on the MW    window and allow the user to interactively modify their values. The    casing points can also be interactively modified on the 2D and 3D    trajectory displays-   7. The system will prompt the user for casing size constraints    (tubing size, surface slot size, evaluation requirements), and based    on the number of sections generate the appropriate hole size—casing    size combinations. The hole/casing circle chart will be used, again    allowing for interaction from the user to modify the hole/casing    size progression.-   8. The system will successively calculate casing grades,    weights/wall thickness and connections based on the sizes selected    and the depths. User will be able to interact and define    availability of types of casing.-   9. The system will generate a basic cementing program, with simple    slurry designs and corresponding volumes.-   10. The system will display the wellbore schematic based on the    calculations previously performed and this interface will be fully    interactive, allowing the user to click and drag hole & casing    sizes, top & bottom setting depths, and recalculating based on these    selections. System will flag user if the selection is not feasible.-   11. The system will generate the appropriate mud types,    corresponding rheology, and composition based on the lithology,    previous calculations, and the users selection.-   12. The system will successively split the well sections into bit    runs, and based on the rock properties will select drilling bits for    each section with ROP and drilling parameters.-   13. The system will generate a basic BHA configuration, based on the    bit section runs, trajectory and rock properties.    Items 14, 15, and 16 represent one task: Hydraulics.-   14. The system will run a hole cleaning calculation, based on    trajectory, wellbore geometry, BHA composition and MW    characteristics.-   15. The system will do an initial hydraulics/ECD calculation using    statistical ROP data. This data will be either selected or user    defined by the system based on smart table lookup.-   16. Using the data generated on the first hydraulics calculation,    the system will perform an ROP simulation based on drilling bit    characteristics and rock properties.-   17. The system will run a successive hydraulics/ECD calculation    using the ROP simulation data. System will flag user if parameters    are not feasible.-   18. The system will calculate the drilling parameters and display    them on a multi display panel. This display will be exportable,    portable, and printable.-   19. The system will generate an activity planning sequence using    default activity sequences for similar hole sections and end    conditions. This sequence will be fully modifiable by the user,    permitting modification in sequence order and duration of the event.    This sequence will be in the same standard as the Well Operations or    Drilling Reporting software and will be interchangeable with the    Well Operations or Drilling Reporting software. The durations of    activities will be populated from tables containing default “best    practice” data or from historical data (DIMS, Snapper . . . ).-   20. The system will generate time vs. depth curve based on the    activity planning details. The system will create a best, mean, and    worst set of time curves using combinations of default and    historical data. These curves will be exportable to other documents    and printable.-   21. The system will prompt the user to select probability points    such as P10, P50, P90 and then run a Monte Carlo simulation to    generate a probability distribution curve for the scenario    highlighting the user selected reference points and corresponding    values of time. The system will provide this as frequency data or    cumulative probability curves. These curves will be again exportable    and printable.-   22. The system will generate a cost plan using default cost    templates that are pre-configured by users and can be modified at    this point. Many of the costs will reference durations of the entire    well, hole sections, or specific activities to calculate the applied    cost. The system will generate P10, P50, and P90 cost vs. depth    curves.-   23. The system will generate a summary of the well plan, in word    format, along with the main display graphs. The user will select all    that should be exported via a check box interface. The system will    generate a large one-page summary of the whole process. This    document will be as per a standard Well Operations Program template.

Referring to FIG. 8, as can be seen on the left side of the displaysillustrated in FIGS. 2 through 6, the ‘Automatic Well Planning SoftwareSystem’ of the present invention includes a plurality of tasks. Each ofthose tasks are illustrated in FIG. 8. In FIG. 8, those plurality oftasks are divided into four groups: (1) Input task 10, where input datais provided, (2) Wellbore Geometry task 12 and Drilling Parameters task14, where calculations are performed, and (3) a Results task 16, where aset of results are calculated and presented to a user. The Input task 10includes the following sub-tasks: (1) scenario information, (2)trajectory, (3) Earth properties, (4) Rig selection, (5) Resample Data.The Wellbore Geometry task 12 includes the following sub-tasks: (1)Wellbore stability, (2) Mud weights and casing points, (3) Wellboresizes, (4) Casing design, (5) Cement design, (6) Wellbore geometry. TheDrilling Parameters task 14 includes the following sub-tasks: (1)Drilling fluids, (2) Bit selection, (3) Drillstring design, (4)Hydraulics. The Results task 16 includes the following sub-tasks: (1)Risk Assessment 16 a, (2) Risk Matrix, (3) Time and cost data, (4) Timeand cost chart, (5) Monte Carlo, (6) Monte Carlo graph, (7) Summaryreport, and (8) montage.

Recalling that the Results task 16 of FIG. 8 includes a ‘RiskAssessment’ sub-task 16 a, the ‘Risk Assessment’ sub-task 16 a will bediscussed in detail in the following paragraphs with reference to FIGS.9A, 9B, and 10.

Automatic Well Planning Software System—Risk Assessment Sub-Task 16a—Software

Identifying the risks associated with drilling a well is probably themost subjective process in well planning today. This is based on aperson recognizing part of a technical well design that is out of placerelative to the earth properties or mechanical equipment to be used todrill the well. The identification of any risks is brought about byintegrating all of the well, earth, and equipment information in themind of a person and mentally sifting through all of the information,mapping the interdependencies, and based solely on personal experienceextracting which parts of the project pose what potential risks to theoverall success of that project. This is tremendously sensitive to humanbias, the individual's ability to remember and integrate all of the datain their mind, and the individuals experience to enable them torecognize the conditions that trigger each drilling risk. Most peopleare not equipped to do this and those that do are very inconsistentunless strict process and checklists are followed. There are somedrilling risk software systems in existence today, but they all requirethe same human process to identify and assess the likelihood of eachindividual risks and the consequences. They are simply a computer systemfor manually recording the results of the risk identification process.

The Risk Assessment sub-task 16 a associated with the ‘Automatic WellPlanning Software System’ of the present invention is a system that willautomatically assess risks associated with the technical well designdecisions in relation to the earth's geology and geomechanicalproperties and in relation to the mechanical limitations of theequipment specified or recommended for use.

Risks are calculated in four ways: (1) by ‘Individual Risk Parameters’,(2) by ‘Risk Categories’, (3) by ‘Total Risk’, and (4) the calculationof ‘Qualitative Risk Indices’ for each.

Individual Risk Parameters are calculated along the measured depth ofthe well and color coded into high, medium, or low risk for display tothe user. Each risk will identify to the user: an explanation of exactlywhat is the risk violation, and the value and the task in the workflowcontrolling the risk. These risks are calculated consistently andtransparently allowing users to see and understand all of the knownrisks and how they are identified. These risks also tell the users whichaspects of the well justify further engineering effort to investigate inmore detail.

Group/category risks are calculated by incorporating all of theindividual risks in specific combinations. Each individual risk is amember of one or more Risk Categories. Four principal Risk Categoriesare defined as follows: (1) Gains, (2) Losses, (3) Stuck, and (4)Mechanical; since these four Rick Categories are the most common andcostly groups of troublesome events in drilling worldwide.

The Total Risk for a scenario is calculated based on the cumulativeresults of all of the group/category risks along both the risk and depthaxes.

Risk indexing—Each individual risk parameter is used to produce anindividual risk index which is a relative indicator of the likelihoodthat a particular risk will occur. This is purely qualitative, butallows for comparison of the relative likelihood of one risk toanother—this is especially indicative when looked at from a percentagechange. Each Risk Category is used to produce a category risk index alsoindicating the likelihood of occurrence and useful for identifying themost likely types of trouble events to expect. Finally, a single riskindex is produced for the scenario that is specifically useful forcomparing the relative risk of one scenario to another.

The ‘Automatic Well Planning Software System’ of the present inventionis capable of delivering a comprehensive technical risk assessment, andit can do this automatically. Lacking an integrated model of thetechnical well design to relate design decisions to associated risks,the ‘Automatic Well Planning Software System’ can attribute the risks tospecific design decisions and it can direct users to the appropriateplace to modify a design choice in efforts to modify the risk profile ofthe well.

Referring to FIG. 9A, a Computer System 18 is illustrated. The ComputerSystem 18 includes a Processor 18 a connected to a system bus, aRecorder or Display Device 18 b connected to the system bus, and aMemory or Program Storage Device 18 c connected to the system bus. TheRecorder or Display Device 18 b is adapted to display ‘Risk AssessmentOutput Data’ 18 b 1. The Memory or Program Storage Device 18 c isadapted to store an ‘Automatic Well Planning Risk Assessment Software’18 c 1. The ‘Automatic Well Planning Risk Assessment Software’ 18 c 1 isoriginally stored on another ‘program storage device’, such as a harddisk; however, the hard disk was inserted into the Computer System 18and the ‘Automatic Well Planning Risk Assessment Software’ 18 c 1 wasloaded from the hard disk into the Memory or Program Storage Device 18 cof the Computer System 18 of FIG. 9A. In addition, a Storage Medium 20containing a plurality of ‘Input Data’ 20 a is adapted to be connectedto the system bus of the Computer System 18, the ‘Input Data’ 20 a beingaccessible to the Processor 18 a of the Computer System 18 when theStorage Medium 20 is connected to the system bus of the Computer System18. In operation, the Processor 18 a of the Computer System 18 willexecute the Automatic Well Planning Risk Assessment Software 18 c 1stored in the Memory or Program Storage Device 18 c of the ComputerSystem 18 while, simultaneously, using the ‘Input Data’ 20 a stored inthe Storage Medium 20 during that execution. When the Processor 18 acompletes the execution of the Automatic Well Planning Risk AssessmentSoftware 18 c 1 stored in the Memory or Program Storage Device 18 c(while using the ‘Input Data’ 20 a), the Recorder or Display Device 18 bwill record or display the ‘Risk Assessment Output Data’ 18 b 1, asshown in FIG. 9A. For example the ‘Risk Assessment Output Data’ 18 b 1can be displayed on a display screen of the Computer System 18, or the‘Risk Assessment Output Data’ 18 b 1 can be recorded on a printout whichis generated by the Computer System 18. The Computer System 18 of FIG.9A may be a personal computer (PC). The Memory or Program Storage Device18 c is a computer readable medium or a program storage device which isreadable by a machine, such as the processor 18 a. The processor 18 amay be, for example, a microprocessor, microcontroller, or a mainframeor workstation processor. The Memory or Program Storage Device 18 c,which stores the ‘Automatic Well Planning Risk Assessment Software’ 18 c1, may be, for example, a hard disk, ROM, CD-ROM, DRAM, or other RAM,flash memory, magnetic storage, optical storage, registers, or othervolatile and/or non-volatile memory.

Referring to FIG. 9B, a larger view of the Recorder or Display Device 18b of FIG. 9A is illustrated. In FIG. 9B, the ‘Risk Assessment OutputData’ 18 b 1 includes:

-   -   a plurality or Risk Categories, (2) a plurality of Subcategory        Risks (each of which have been ranked as either a High Risk or a        Medium Risk or a Low Risk), and (3) a plurality of Individual        Risks (each of which have been ranked as either a High Risk or a        Medium Risk or a Low Risk). The Recorder or Display Device 18 b        of FIG. 9B will display or record the ‘Risk Assessment Output        Data’ 18 b 1 including the Risk Categories, the Subcategory        Risks, and the Individual Risks.

Referring to FIG. 10, a detailed construction of the ‘Automatic WellPlanning Risk Assessment Software’ 18 c 1 of FIG. 9A is illustrated. InFIG. 10, the ‘Automatic Well Planning Risk Assessment Software’ 18 c 1includes a first block which stores the Input Data 20 a, a second block22 which stores a plurality of Risk Assessment Logical Expressions 22; athird block 24 which stores a plurality of Risk Assessment Algorithms24, a fourth block 26 which stores a plurality of Risk AssessmentConstants 26, and a fifth block 28 which stores a plurality of RiskAssessment Catalogs 28. The Risk Assessment Constants 26 include valueswhich are used as input for the Risk Assessment Algorithms 24 and theRisk Assessment Logical Expressions 22. The Risk Assessment Catalogs 28include look-up values which are used as input by the Risk AssessmentAlgorithms 24 and the Risk Assessment Logical Expressions 22. The ‘InputData’ 20 a includes values which are used as input for the RiskAssessment Algorithms 24 and the Risk Assessment Logical Expressions 22.The ‘Risk Assessment Output Data’ 18 b 1 includes values which arecomputed by the Risk Assessment Algorithms 24 and which result from theRisk Assessment Logical Expressions 22. In operation, referring to FIGS.9 and 10, the Processor 18 a of the Computer System 18 of FIG. 9Aexecutes the Automatic Well Planning Risk Assessment Software 18 c 1 byexecuting the Risk Assessment Logical Expressions 22 and the RiskAssessment Algorithms 24 of the Risk Assessment Software 18 c 1 while,simultaneously, using the ‘Input Data’ 20 a, the Risk AssessmentConstants 26, and the values stored in the Risk Assessment Catalogs 28as ‘input data’ for the Risk Assessment Logical Expressions 22 and theRisk Assessment Algorithms 24 during that execution. When that executionby the Processor 18 a of the Risk Assessment Logical Expressions 22 andthe Risk Assessment Algorithms 24 (while using the ‘Input Data’ 20 a,Constants 26, and Catalogs 28) is completed, the ‘Risk Assessment OutputData’ 18 b 1 will be generated as a ‘result’. That ‘Risk AssessmentOutput Data’ 18 b 1 is recorded or displayed on the Recorder or DisplayDevice 18 b of the Computer System 18 of FIG. 9A. In addition, that‘Risk Assessment Output Data’ 18 b 1 can be manually input, by anoperator, to the Risk Assessment Logical Expressions block 22 and theRisk Assessment Algorithms block 24 via a ‘Manual Input’ block 30 shownin FIG. 10.

Input Data 20 a

The following paragraphs will set forth the ‘Input Data’ 20 a which isused by the ‘Risk Assessment Logical Expressions’ 22 and the ‘RiskAssessment Algorithms’ 24. Values of the Input Data 20 a that are usedas input for the Risk Assessment Algorithms 24 and the Risk AssessmentLogical Expressions 22 are as follows:

-   -   (1) Casing Point Depth    -   (2) Measured Depth    -   (3) True Vertical Depth    -   (4) Mud Weight    -   (5) Measured Depth    -   (6) ROP    -   (7) Pore Pressure    -   (8) Static Temperature    -   (9) Pump Rate    -   (10) Dog Leg Severity    -   (11) ECD    -   (12) Inclination    -   (13) Hole Size    -   (14) Casing Size    -   (15) Easting-westing    -   (16) Northing-Southing    -   (17) WaterDepth    -   (18) Maximum Water Depth    -   (19) Maximum well Depth    -   (20) Kick Tolerance    -   (21) Drill Collar 1 Weight    -   (22) Drill Collar 2 Weight    -   (23) Drill Pipe Weight    -   (24) Heavy Weight Weight    -   (25) Drill Pipe Tensile Rating    -   (26) Upper Wellbore Stability Limit    -   (27) Lower Wellbore Stability Limit    -   (28) Unconfined Compressive Strength    -   (29) Bit Size    -   (30) Mechanical drilling energy (UCS integrated over distance        drilled by the bit)    -   (31) Ratio of footage drilled compared to statistical footage    -   (32) Cumulative UCS    -   (33) Cumulative Excess UCS    -   (34) Cumulative UCS Ratio    -   (35) Average UCS of rock in section    -   (36) Bit Average UCS of rock in section    -   (37) Statistical Bit Hours    -   (38) Statistical Drilled Footage for the bit    -   (39) RPM    -   (40) On Bottom Hours    -   (41) Calculated Total Bit Revolutions    -   (42) Time to Trip    -   (43) Critical Flow Rate    -   (44) Maximum Flow Rate in hole section    -   (45) Minimum Flow Rate in hole section    -   (46) Flow Rate    -   (47) Total Nozzle Flow Area of bit    -   (48) Top Of Cement    -   (49) Top of Tail slurry    -   (50) Length of Lead slurry    -   (51) Length of Tail slurry    -   (52) Cement Density Of Lead    -   (53) Cement Density Of Tail slurry    -   (54) Casing Weight per foot    -   (55) Casing Burst Pressure    -   (56) Casing Collapse Pressure    -   (57) Casing Type Name    -   (58) Hydrostatic Pressure of Cement column    -   (59) Start Depth    -   (60) End Depth    -   (61) Conductor    -   (62) Hole Section Begin Depth    -   (63) Openhole Or Cased hole completion    -   (64) Casing Internal Diameter    -   (65) Casing Outer Diameter    -   (66) Mud Type    -   (67) Pore Pressure without Safety Margin    -   (68) Tubular Burst Design Factor    -   (69) Casing Collapse Pressure Design Factor    -   (70) Tubular Tension Design Factor    -   (71) Derrick Load Rating    -   (72) Drawworks Rating    -   (73) Motion Compensator Rating    -   (74) Tubular Tension rating    -   (75) Statistical Bit ROP    -   (76) Statistical Bit RPM    -   (77) Well Type    -   (78) Maximum Pressure    -   (79) Maximum Liner Pressure Rating    -   (80) Circulating Pressure    -   (81) Maximum UCS of bit    -   (82) Air Gap    -   (83) Casing Point Depth    -   (84) Presence of H2S    -   (85) Presence of CO2    -   (86) Offshore Well    -   (87) Flow Rate Maximum Limit        Risk Assessment Constants 26

The following paragraphs will set forth the ‘Risk Assessment Constants’26 which are used by the ‘Risk Assessment Logical Expressions’ 22 andthe ‘Risk Assessment Algorithms’ 24. Values of the Constants 26 that areused as input data for Risk Assessment Algorithms 24 and the RiskAssessment Logical Expressions 22 are as follows:

-   -   (1) Maximum Mud Weight Overbalance to Pore Pressure    -   (2) Minimum Required Collapse Design Factor    -   (3) Minimum Required Tension Design Factor    -   (4) Minimum Required Burst Design Factor    -   (5) Rock density    -   (6) Seawater density        Risk Assessment Catalogs 28

The following paragraphs will set forth the ‘Risk Assessment Catalogs’28 which are used by the ‘Risk Assessment Logical Expressions’ 22 andthe ‘Risk Assessment Algorithms’ 24. Values of the Catalogs 28 that areused as input data for Risk Assessment Algorithms 24 and the RiskAssessment Logical Expressions 22 include the following:

-   -   (1) Risk Matrix Catalog    -   (2) Risk Calculation Catalog    -   (3) Drillstring component catalog    -   (4) Drill Bit Catalog    -   (5) Clearance Factor Catalog    -   (6) Drill Collar Catalog    -   (7) Drill Pipes Catalog    -   (8) Minimum and maximum flow rate catalog    -   (9) Pump catalog    -   (10) Rig Catalog    -   (11) Constants and variables Settings catalog    -   (12) Tubular Catalog        Risk Assessment Output Data 18 b 1

The following paragraphs will set forth the ‘Risk Assessment OutputData’ 18 b 1 which are generated by the ‘Risk Assessment Algorithms’ 24.The ‘Risk Assessment Output Data’ 18 b 1, which is generated by the‘Risk Assessment Algorithms’ 24, includes the following types of outputdata: (1) Risk Categories, (2) Subcategory Risks, and (3) IndividualRisks. The ‘Risk Categories’, ‘Subcategory Risks’, and ‘IndividualRisks’ included within the ‘Risk Assessment Output Data’ 18 b 1 comprisethe following:

The following ‘Risk Categories’ are calculated:

-   -   (1) Individual Risk    -   (2) Average Individual Risk    -   (3) Subcategory Risk    -   (4) Average Subcategory Risk    -   (5) Total risk    -   (6) Average total risk    -   (7) Potential risk for each design task    -   (8) Actual risk for each design task

The following ‘Subcategory Risks’ are calculated

-   -   (1) Gains risks    -   (2) Losses risks    -   (3) Stuck Pipe risks    -   (4) Mechanical risks

The following ‘Individual Risks’ are calculated

-   -   (1) H2S and CO2,    -   (2) Hydrates,    -   (3) Well water depth,    -   (4) Tortuosity,    -   (5) Dogleg severity,    -   (6) Directional Drilling Index,    -   (7) Inclination,    -   (8) Horizontal displacement,    -   (9) Casing Wear,    -   (10) High pore pressure,    -   (11) Low pore pressure,    -   (12) Hardrock,    -   (13) Soft Rock,    -   (14) High temperature,    -   (15) Water-depth to rig rating,    -   (16) Well depth to rig rating,    -   (17) mud weight to kick,    -   (18) mud weight to losses,    -   (19) mud weight to fracture,    -   (20) mud weight window,    -   (21) Wellbore stability window,    -   (22) wellbore stability,    -   (23) Hole section length,    -   (24) Casing design factor,    -   (25) Hole to casing clearance,    -   (26) casing to casing clearance,    -   (27) casing to bit clearance,    -   (28) casing linear weight,    -   (29) Casing maximum overpull,    -   (30) Low top of cement,    -   (31) Cement to kick,    -   (32) cement to losses,    -   (33) cement to fracture,    -   (34) Bit excess work,    -   (35) Bit work,    -   (36) Bit footage,    -   (37) bit hours,    -   (38) Bit revolutions,    -   (39) Bit ROP,    -   (40) Drillstring maximum overputt,    -   (41) Bit compressive strength,    -   (42) Kick tolerance,    -   (43) Critical flow rate,    -   (44) Maximum flow rate,    -   (45) Small nozzle area,    -   (46) Standpipe pressure,    -   (47) ECD to fracture,    -   (48) ECD to losses,    -   (49) Subsea BOP,    -   (50) Large Hole,    -   (51) Small Hole,    -   (52) Number of casing strings,    -   (53) Drillstring parting,    -   (54) Cuttings.        Risk Assessment Logical Expressions 22

The following paragraphs will set forth the ‘Risk Assessment LogicalExpressions’ 22. The ‘Risk Assessment Logical Expressions’ 22 will: (1)receive the ‘Input Data 20 a’ including a ‘plurality of Input Datacalculation results’ that has been generated by the ‘Input Data 20 a’;(2) determine whether each of the ‘plurality of Input Data calculationresults’ represent a high risk, a medium risk, or a low risk; and (3)generate a ‘plurality of Risk Values’ (also known as a ‘plurality ofIndividual Risks), in response thereto, each of the plurality of RiskValues/plurality of Individual Risks representing a ‘an Input Datacalculation result’ that has been ‘ranked’ as either a ‘high risk’, a‘medium risk’, or a ‘low risk’.

The Risk Assessment Logical Expressions 22 include the following:

-   Task: Scenario-   Description: H2S and CO2 present for scenario indicated by user (per    well)-   Short Name: H2S_CO2-   Data Name: H2S-   Calculation: H2S and CO2 check boxes checked yes-   Calculation Name: Calculate H2S_CO2-   High: Both selected-   Medium: Either one selected-   Low: Neither selected-   Unit: unitless-   Task: Scenario-   Description: Hydrate development (per well)-   Short Name: Hydrates-   Data Name: Water Depth-   Calculation: =Water Depth-   Calculation Name: CalculateHydrates-   High: >=3000-   Medium: >=2000-   Low: <2000-   Unit: ft-   Task: Scenario-   Description: Hydrate development (per well)-   Short Name: Well_WD-   Data Name: Water Depth-   Calculation: =WaterDepth-   Calculation Name: CalculateHydrates-   High: >=5000-   Medium: >=1000-   Low: <1000-   Unit: ft-   Task: Trajectory-   Description: Dogleg severity (per depth)-   Short Name: DLS-   Data Name: Dog Leg Severity-   Calculation: NA-   Calculation Name: CalculateRisk-   High: >=6-   Medium: >=4-   Low: <4-   Unit: deg/100 ft-   Task: Trajectory-   Description: Tortuosity (per depth)-   Short Name: TORT-   Data Name: Dog Leg Severity-   Calculation: Summation of DLS-   Calculation Name: CalculateTort-   High: >=90-   Medium: >=60-   Low: <60-   Unit: deg-   Task: Trajectory-   Description: Inclination (per depth)-   Short Name: INC-   Data Name: Inclination-   Calculation: NA-   Calculation Name: CalculateRisk-   High: >=65-   Medium: >=40-   Low: <40-   Unit: deg-   Task: Trajectory-   Description: Well inclinations with difficult cuttings transport    conditions (per depth)-   Short Name: Cutting-   Data Name: Inclination-   Calculation: NA-   Calculation Name: CalculateCutting-   High: >=45-   Medium: >65-   Low: <45-   Unit: deg-   Task: Trajectory-   Description: Horizontal to vertical ratio (per depth)-   Short Name: Hor_Disp-   Data Name: Inclination-   Calculation: =Horizontal Displacement/True Vertical Depth-   Calculation Name: CalculateHor Disp-   High: >=1.0-   Medium: >=0.5-   Low: <0.5-   Unit: Ratio-   Task: Trajectory-   Description: Directional Drillability Index (per depth) Fake    Threshold-   Short Name: DDI-   Data Name: Inclination-   Calculation: =Calculate DDI using Resample data-   Calculation Name: CalculateDDI-   High: >6.8-   Medium: >=6.0-   Low: <6.0-   Unit: unitless-   Task: EarthModel-   Description: High or supernormal Pore Pressure (per depth)-   Short Name: PP_High-   Data Name: Pore Pressure without Safety Margin-   Calculation: =PP-   Calculation Name: CalculateRisk-   High: >=16-   Medium: >=12-   Low: <12-   Unit: ppg-   Task: EarthModel-   Description: Depleted or subnormal Pore Pressure (per depth)-   Short Name: PP_Low-   Data Name: Pore Pressure without Safety Margin-   Calculation: =Pore Pressure without Safety Margin-   Calculation Name: CalculateRisk-   High: <=8.33-   Medium: <=8.65-   Low: >8.65-   Unit: ppg-   Task: EarthModel-   Description: Superhard rock (per depth)-   Short Name: RockHard-   Data Name: Unconfined Compressive Strength-   Calculation: =Unconfined Compressive Strength-   Calculation Name: CalculateRisk-   High: >=25-   Medium: >=16-   Low: <16-   Unit: kpsi-   Task: EarthModel-   Description: Gumbo (per depth)-   Short Name: RockSoft-   Data Name: Unconfined Compressive Strength-   Calculation: =Unconfined Compressive Strength-   Calculation Name: CalculateRisk-   High: <=2-   Medium: <=4-   Low: >4-   Unit: kpsi-   Task: EarthModel-   Description: High Geothermal Temperature (per depth)-   Short Name: TempHigh-   Data Name: StaticTemperature-   Calculation: =Temp-   Calculation Name: CalculateRisk-   High: >=280-   Medium: >=220-   Low: <220-   Unit: degF-   Task: RigConstraint-   Description: Water depth as a ratio to the maximum water depth    rating of the rig (per depth)-   Short Name: Rig_WD-   Data Name:-   Calculation: =WD, Rig WD rating-   Calculation Name: CalculateRig_WD-   High: >=0.75-   Medium: >=0.5-   Low: <0.5-   Unit: Ratio-   Task: RigConstraint-   Description: Total measured depth as a ratio to the maximum depth    rating of the rig (per depth)-   Short Name: Rig_MD-   Data Name:-   Calculation: =MD/Rig MD rating-   Calculation Name: CalculateRig_MD-   High: >=0.75-   Medium: >=0.5-   Low: <0.5-   Unit: Ratio-   Task: RigConstraint-   “Description: Subsea BOP or wellhead (per well), not quite sure how    to compute it”-   Short Name: SS_BOP-   Data Name: Water Depth-   Calculation: =-   Calculation Name: CalculateHydrates-   High: >=3000-   Medium: >=1000-   Low: <1000-   Unit: ft-   Task: MudWindow-   Description: Kick potential where Mud Weight is too low relative to    Pore Pressure (per depth)-   Short Name: MW_Kick-   Data Name:-   Calculation: =Mud Weight−Pore Pressure-   Calculation Name: CalculateMW_Kick-   High: <=0.3-   Medium: <=0.5-   Low: >0.5-   Unit: ppg-   Task: MudWindow-   Description: Loss potential where Hydrostatic Pressure is too high    relative to Pore Pressure (per depth)-   Short Name: MW_Loss-   DataName:-   Calculation: =Hydrostatic Pressure−Pore Pressure-   Calculation Name: CalculateMW_Loss-   “PreCondition: =Mud Type (HP-WBM, ND-WBM, D-WBM)”-   High: >=2500-   Medium: >=2000-   Low: <2000-   Unit: psi-   Task: MudWindow-   Description: Loss potential where Hydrostatic Pressure is too high    relative to Pore Pressure (per depth)-   Short Name: MW_Loss-   Data Name:-   Calculation: =Hydrostatic Pressure−Pore Pressure-   Calculation Method: CalculateMW_Loss-   “PreCondition: =Mud Type (OBM, MOBM, SOBM)”-   High: >=2000-   Medium: >=1500-   Low: <1500-   Unit: psi-   Task: MudWindow-   Description: Loss potential where Mud Weight is too high relative to    Fracture Gradient (per depth)-   Short Name: MW_Frac-   Data Name:-   Calculation: =Upper Bound−Mud Weight-   Calculation Method: CalculateMW_Frac-   High: <=0.2-   Medium: <=0.5-   Low: >0.5-   Unit: ppg-   Task: MudWindow-   Description: Narrow mud weight window (per depth)-   Short Name: MWW-   Data Name:-   Calculation: =Upper Wellbore Stability Limit−Pore Pressure without    Safety Margin-   Calculation Method: CalculateMWW-   High: <=0.5-   Medium: <=1.0-   Low: >1.0-   Unit: ppg-   Task: MudWindow-   Description: Narrow wellbore stability window (per depth)-   Short Name: WBSW-   Data Name:-   Calculation: =Upper Bound−Lower Bound-   Calculation Method: CalculateWBSW-   “PreCondition: =Mud Type (OBM, MOBM, SOBM)”-   High: <=0.3-   Medium: <=0.6-   Low: >0.6-   Unit: ppg-   Task: MudWindow-   Description: Narrow wellbore stability window (per depth)-   Short Name: WBSW-   Data Name:-   Calculation: =Upper Bound−Lower Bound-   Calculation Method: CalculateWBSW-   “PreCondition: =Mud Type (HP-WBM, ND-WBM, D-WBM)”-   High: <=0.4-   Medium: <=0.8-   Low: >0.8-   Unit: ppg-   Task: MudWindow-   Description: Wellbore Stability (per depth)-   Short Name: WBS-   Data Name: Pore Pressure without Safety Margin-   Calculation: =Pore Pressure without Safety Margin-   Calculation Method: CalculateWBS-   High: LB>=MW>=PP-   Medium: MW>=LB>=PP-   Low: MW>=PP>=LB-   Unit: unitless-   Task: MudWindow-   Description: Hole section length (per hole section)-   Short Name: HSLength-   Data Name:-   Calculation: =HoleEnd−HoleStart-   Calculation Method: CalculateHSLength-   High: >=8000-   Medium: >=7001-   Low: <7001-   Unit: ft-   Task: MudWindow-   Description: Dogleg severity at Casing points for casing wear (per    hole section)-   Short Name: Csg_Wear-   Data Name: Dog Leg Severity-   Calculation: =Hole diameter-   Calculation Method: CalculateCsg_Wear-   High: >=4-   Medium: >=3-   Low: <3-   Unit: deg/100 ft-   Task: MudWindow-   Description: Number of Casing strings (per hole section)-   Short Name: Csg_Count-   Data Name: Casing Point Depth-   Calculation: =Number of Casing strings-   Calculation Method: CalculateCsg_Count-   High: >=6-   Medium: >=4-   Low: <4-   Unit: unitless-   Task: WellboreSizes-   Description: Large Hole size (per hole section)-   Short Name: Hole_Big-   Data Name: Hole Size-   Calculation: =Hole diameter-   Calculation Method: CalculateHoleSectionRisk-   High: >=24-   Medium: >=18.625-   Low: <18.625-   Unit: in-   Task: WellboreSizes-   Description: Small Hole size (per hole section)-   Short Name: Hole_Sm-   Data Name: Hole Size-   Calculation: =Hole diameter-   Calculation Method: CalculateHole_Sm-   PreCondition: Onshore-   High: <=4.75-   Medium: <=6.5-   Low: >6.5-   Unit: in-   Task: WellboreSizes-   Description: Small Hole size (per hole section)-   Short Name: Hole_Sm-   Data Name: Hole Size-   Calculation: =Hole diameter-   Calculation Method: CalculateHole_Sm-   PreCondition: Offshore-   High: <=6.5-   Medium: <=7.875-   Low: >7.875-   Unit: in-   Task: TubularDesign-   “Description: Casing Design Factors for Burst, Collapse, & Tension    (per hole section), DFb,c,t<=1.0 for High, DFb,c,t<=1.1 for Medium,    DFb,c,t>1.1 for Low”-   Short Name: Csg_DF-   Data Name:-   Calculation: =DF/Design Factor-   Calculation Method: CalculateCsg_DF-   High: <=1.0-   Medium: <=1.1-   Low: >1.1-   Unit: unitless-   Task: TubularDesign-   Description: Casing string weight relative to rig lifting    capabilities (per casing string)-   Short Name: Csg_Wt-   Data Name:-   Calculation: =CasingWeightVRigMinRating-   Calculation Method: CalculateCsg_Wt-   High: >=0.95-   Medium: <0.95-   Low: <0.8-   Unit: Ratio-   Task: TubularDesign-   Description: Casing string allowable Margin of Overpull (per casing    string)-   Short Name: Csg_MOP-   Data Name:-   Calculation: =Tubular Tension rating−CasingWeight-   Calculation Method: CalculateCsg_MOP-   High: <=50-   Medium: <=100-   Low: >100-   Unit: klbs-   Task: WellboreSizes-   Description: Clearance between hole size and casing max OD (per hole    section)-   Short Name: Hole_Csg-   Data Name:-   Calculation: =Area of hole size, Area of casing size (max OD)-   Calculation Method: CalculateHole_Csg-   High: <=1.1-   Medium: <=1.25-   Low: >1.25-   Unit: Ratio-   Task: WellboreSizes-   Description:-   Short Name: Csg_Csg-   Data Name:-   Calculation: =CainsgID/NextMaxCasingSize-   Calculation Method: CalculateCsg_Csg-   High: <=1.05-   Medium: <=1.1-   Low: >1.1-   Unit: Ratio-   Task: WellboreSizes-   Description: Clearance between casing inside diameter and subsequent    bit size (per bit run)-   Short Name: Csg_Bit-   Data Name:-   Calculation: =CainsgID/NextBit Size-   Calculation Method: CalculateCsg_Bit-   High: <=1.05-   Medium: <=1.1-   Low: >1.1-   Unit: Ratio-   Task: CementDesign-   Description: Cement height relative to design guidelines for each    string type (per hole section)-   Short Name: TOC_Low-   Data Name:-   Calculation: =CasingBottomDepth−TopDepthOfCement-   Calculation Method: CalculateTOC_Low-   High: <=0.75-   Medium: <=1.0-   Low: >1.0-   Unit: Ratio-   Task: CementDesign-   Description: Kick potential where Hydrostatic Pressure is too low    relative to Pore Pressure (per depth)-   Short Name: Cmt_Kick-   Data Name:-   Calculation: =( Cementing Hydrostatic Pressure−Pore Pressure)/TVD-   Calculation Method: CalculateCmt_Kick-   High: <=0.3-   Medium: <=0.5-   Low: >0.5-   Unit: ppg-   Task: CementDesign-   Description: Loss potential where Hydrostatic Pressure is too high    relative to Pore Pressure (per depth)-   Short Name: Cmt_Loss-   Data Name:-   Calculation: =Cementing Hydrostatic Pressure−Pore Pressure-   Calculation Method: CalculateCmt_Loss-   High: >=2500-   Medium: >=2000-   Low: <2000-   Unit: psi-   Task: CementDesign-   Description: Loss potential where Hydrostatic Pressure is too high    relative to Fracture Gradient (per depth)-   Short Name: Cmt_Frac-   Data Name:-   Calculation: =(UpperBound−Cementing Hydrostatic Pressure)/TVD-   Calculation Method: CalculateCmt_Frac-   High: <=0.2-   Medium: <=0.5-   Low: >0.5-   Unit: ppg-   Task: BitsSelection-   Description: Excess bit work as a ratio to the Cumulative Mechanical    drilling energy (UCS integrated over distance drilled by the bit)-   Short Name: Bit_WkXS-   Data Name: CumExcessCumulative UCSRatio-   Calculation: =CumExcess/Cumulative UCS-   Calculation Method: CalculateBitSectionRisk-   High: >=0.2-   Medium: >=0.1-   Low: <0.1-   Unit: Ratio-   Task: BitsSelection-   Description: Cumulative bit work as a ratio to the bit catalog    average Mechanical drilling energy (UCS integrated over distance    drilled by the bit)-   Short Name: Bit_Wk-   Data Name:-   Calculation: =Cumulative UCS/ Mechanical drilling energy (UCS    integrated over distance drilled by the bit)-   Calculation Method: CalculateBit_Wk-   High: >=1.5-   Medium: >=1.25-   Low: <1.25-   Unit: Ratio-   Task: BitsSelection-   Description: Cumulative bit footage as a ratio to the bit catalog    average footage (drilled length) (per depth)-   Short Name: Bit_Ftg-   Data Name: Ratio of footage drilled compared to statistical footage-   Calculation: =Ratio of footage drilled compared to statistical    footage-   Calculation Method: CalculateBitSectionRisk-   High: >=2-   Medium: >=1.5-   Low: <1.5-   Unit: Ratio-   Task: BitsSelection-   Description: Cumulative bit hours as a ratio to the bit catalog    average hours (on bottom rotating time) (per depth)-   Short Name: Bit_Hrs-   Data Name: Bit_Ftg-   Calculation: =On Bottom Hours/Statistical Bit Hours-   Calculation Method: CalculateBit_Hrs-   High: >=2-   Medium: >=1.5-   Low: <1.5-   Unit: Ratio-   Task: BitsSelection-   Description: Cumulative bit Krevs as a ratio to the bit catalog    average Krevs (RPM*hours) (per depth)-   Short Name: Bit_Krev-   Data Name:-   Calculation: =Cumulative Krevs Bit average Krevs-   Calculation Method: CalculateBit Krev-   High: >=2-   Medium: >=1.5-   Low: <1.5-   Unit: Ratio-   Task: BitsSelection-   Description: Bit ROP as a ratio to the bit catalog average ROP (per    bit run)-   Short Name: Bit_ROP-   Data Name:-   Calculation: =ROP/Statistical Bit ROP-   Calculation Method: CalculateBit_ROP-   High: >=1.5-   Medium: >=1.25-   Low: <1.25-   Unit: Ratio-   Task: BitsSelection-   Description: UCS relative to Bit UCS and Max Bit UCS (per depth)-   Short Name: Bit_UCS-   Data Name:-   Calculation: =UCS-   Calculation Method: CalculateBit_UCS-   High: UCS>=Max Bit UCS>=Bit UCS-   Medium: Max Bit UCS>=UCS>=Bit UCS-   Low: Max Bit UCS>=Bit UCS>=UCS-   Unit: Ratio-   Task: DrillstringDesign-   Description: Drillstring allowable Margin of Overpull (per bit run)-   Short Name: DS_MOP-   Data Name:-   Calculation: =MOP-   Calculation Method: CalculateDS_MOP-   High: <=50-   Medium: <=100-   Low: >100-   Unit: klbs-   Task: DrillstringDesign-   “Description: Potential parting of the drillstrings where required    tension approaches mechanical tension limits of drill pipe, heavy    weight, drill pipe, drill collars, or connections (per bit run)”-   Short Name: DS_Part-   Data Name:-   Calculation: =Required Tension (including MOP)/Tension limit of    drilling component (DP)-   Calculation Method: CalculateDS_Part-   High: >=0.9-   Medium: >=0.8-   Low: >0.8-   Unit: ratio-   Task: DrillstringDesign-   Description: Kick Tolerance (per hole section)-   Short Name: Kick_Tol-   DataName: Bit_UCS-   “Calculation: NA (already calculated), Exploration/Development”-   Calculation Method: CalculateKick_Tol-   PreCondition: Exporation-   High: <=50-   Medium: <=100-   Low: >100-   Unit: bbl-   Task: DrillstringDesign-   Description: Kick Tolerance (per hole section)-   Short Name: Kick_Tol-   Data Name: Bit_UCS-   “Calculation: NA (already calculated), Exploration/Development”-   Calculation Method: CalculateKick_Tol-   PreCondition: Development-   High: <=25-   Medium: <=50-   Low: >50-   Unit: bbl-   Task: Hydraulics-   Description: Flow rate for hole cleaning (per depth)-   Short Name: QCrit-   “Data Name: Flow Rate, Critical Flow Rate”-   Calculation: =Flow Rate/Critical Flow Rate-   Calculation Method: CalculateQCrit-   High: <=1.0-   Medium: <=1.1-   Low: >1.1-   Unit: Ratio-   Task: Hydraulics-   Description: Flow rate relative to pump capabilities(per depth)-   Short Name: Q_Max-   Data Name: Bit_UCS-   Calculation: =Q/Qmax-   Calculation Method: CalculateQMax-   High: >=1.0-   Medium: >=0.9-   Low: <0.9-   Unit: Ratio-   Task: Hydraulics-   “Description: TFA size relative to minimum TFA (per bit run),    0.2301=3 of 10/32 inch, 0.3313=3 of 12/32inch”-   Short Name: TFA_Low-   Data Name: Bit_UCS-   Calculation: TFA-   Calculation Method: CalculateTFA Low-   High: <=0.2301-   Medium: <=0.3313-   Low: >0.3313-   Unit: inch-   Task: Hydraulics-   Description: Circulating pressure relative to rig and pump maximum    pressure (per depth)-   Short Name: P_Max-   Data Name: Bit_UCS-   Calculation: P_Max-   Calculation Method: CalculateP_Max-   High: >=1.0-   Medium: >=0.9-   Low: <0.9-   Unit: Ratio-   Task: Hydraulics-   Description: Loss potential where ECD is too high relative to    Fracture Gradient (per depth)-   Short Name: ECD_Frac-   Data Name: Bit_UCS-   Calculation: UpperBound-ECD-   Calculation Method: CalculateECD_Frac-   High: <=0.0-   Medium: <=0.2-   Low: >0.2-   Unit: ppg-   Task: Hydraulics-   Description: Loss potential where ECD is too high relative to Pore    Pressure (per depth)-   Short Name: ECD_Loss-   Data Name: Bit_UCS-   Calculation: =ECD−Pore Pressure-   Calculation Method: CalculateECD_Loss-   “PreCondition: Mud Type (HP-WBM, ND-WBM, D-WBM)”-   High: >=2500-   Medium: >=2000-   Low: <2000-   Unit: psi-   Task: Hydraulics-   Description: Loss potential where ECD is too high relative to Pore    Pressure (per depth)-   Short Name: ECD_Loss-   Data Name: Bit_UCS-   Calculation: =ECD−Pore Pressure-   Calculation Method: CalculateECD_Loss-   “PreCondition: Mud Type (OBM, MOBM, SOBM)”-   High: >=2000-   Medium: >=1500-   Low: <1500-   Unit: psi    Risk Assessment Algorithms 24

Recall that the ‘Risk Assessment Logical Expressions’ 22 will: (1)receive the ‘Input Data 20 a’ including a ‘plurality of Input Datacalculation results’ that has been generated by the ‘Input Data 20 a’;(2) determine whether each of the ‘plurality of Input Data calculationresults’ represent a high risk, a medium risk, or a low risk; and (3)generate a plurality of Risk Values/plurality of Individual Risks inresponse thereto, where each of the plurality of Risk Values/pluralityof Individual Risks represents a ‘an Input Data calculation result’ thathas been ‘ranked’ as either a ‘high risk’, a ‘medium risk’, or a ‘lowrisk’. For example, recall the following task:

-   Task: Hydraulics-   Description: Loss potential where ECD is too high relative to Pore    Pressure (per depth)-   Short Name: ECD_Loss-   Data Name: Bit_UCS-   Calculation: =ECD−Pore Pressure-   Calculation Method: CalculateECD_Loss-   “PreCondition: Mud Type (OBM, MOBM, SOBM)”-   High: >=2000-   Medium: >=1500-   Low: <1500-   Unit: psi

When the Calculation ‘ECD−Pore Pressure’ associated with the abovereferenced Hydraulics task is >=2000, a ‘high’ rank is assigned to thatcalculation; but if the Calculation ‘ECD−Pore Pressure’ is >=1500, a‘medium’ rank is assigned to that calculation, but if the Calculation‘ECD−Pore Pressure’ is <1500, a ‘low’ rank is assigned to thatcalculation.

Therefore, the ‘Risk Assessment Logical Expressions’ 22 will rank eachof the ‘Input Data calculation results’ as either a ‘high risk’ or a‘medium risk’ or a ‘low risk’ thereby generating a ‘plurality of rankedRisk Values’, also known as a ‘plurality of ranked Individual Risks’. Inresponse to the ‘plurality of ranked Individual Risks’ received from theLogical Expressions 22, the ‘Risk Assessment Logical Algorithms’ 24 willthen assign a ‘value’ and a ‘color’ to each of the plurality of rankedIndividual Risks received from the Logical Expressions 22, where the‘value’ and the ‘color’ depends upon the particular ranking (i.e., the‘high risk’ rank, or the ‘medium risk’ rank, or the ‘low risk’ rank)that is associated with each of the plurality of ranked IndividualRisks. The ‘value’ and the ‘color’ is assigned, by the ‘Risk AssessmentAlgorithms’ 24, to each of the plurality of Individual Risks receivedfrom the Logical Expressions 22 in the following manner:

Risk Calculation #1—Individual Risk Calculation:

Referring to the ‘Risk Assessment Output Data’ 18 b 1 set forth above,there are fifty-four (54) ‘Individual Risks’ currently specified. For an‘Individual Risk’:

-   a High risk=90,-   a Medium risk=70, and-   a Low risk=10-   High risk color code=Red-   Medium risk color code=Yellow-   Low risk color code=Green

If the ‘Risk Assessment Logical Expressions’ 22 assign a ‘high risk’rank to a particular ‘Input Data calculation result’, the ‘RiskAssessment Algorithms’ 24 will then assign a value ‘90’ to that ‘InputData calculation result’ and a color ‘red’ to that ‘Input Datacalculation result’.

If the ‘Risk Assessment Logical Expressions’ 22 assign a ‘medium risk’rank to a particular ‘Input Data calculation result’, the ‘RiskAssessment Algorithms’ 24 will then assign a value ‘70’ to that ‘InputData calculation result’ and a color ‘yellow’ to that ‘Input Datacalculation result’.

If the ‘Risk Assessment Logical Expressions’ 22 assign a ‘low risk’ rankto a particular ‘Input Data calculation result’, the ‘Risk AssessmentAlgorithms’ 24 will then assign a value ‘10’ to that ‘Input Datacalculation result’ and a color ‘green’ to that ‘Input Data calculationresult’.

Therefore, in response to the ‘Ranked Individual Risks’ from the LogicalExpressions 22, the Risk Assessment Algorithms 24 will assign to each ofthe ‘Ranked Individual Risks’ a value of 90 and a color ‘red’ for a highrisk, a value of 70 and a color ‘yellow’ for the medium risk, and avalue of 10 and a color ‘green’ for the low risk. However, in addition,in response to the ‘Ranked Individual Risks’ from the LogicalExpressions 22, the Risk Assessment Algorithms 24 will also generate aplurality of ranked ‘Risk Categories’ and a plurality of ranked‘Subcategory Risks’

Referring to the ‘Risk Assessment Output Data’ 18 b 1 set forth above,the ‘Risk Assessment Output Data’ 18 b 1 includes: (1) eight ‘RiskCategories’, (2) four ‘Subcategory Risks’, and (3) fifty-four (54)‘Individual Risks’ [that is, 54 individual risks plus 2 ‘gains’ plus 2‘losses’ plus 2 ‘stuck’ plus 2 ‘mechanical’ plus 1 ‘total’=63 risks].

The eight ‘Risk Categories’ include the following: (1) an IndividualRisk, (2) an Average Individual Risk, (3) a Risk Subcategory (orSubcategory Risk), (4) an Average Subcategory Risk, (5) a Risk Total (orTotal Risk), (6) an Average Total Risk, (7) a potential Risk for eachdesign task, and (8) an Actual Risk for each design task.

Recalling that the ‘Risk Assessment Algorithms’ 24 have alreadyestablished and generated the above referenced ‘Risk Category (1)’[i.e., the plurality of ranked Individual Risks’] by assigning a valueof 90 and a color ‘red’ to a high risk ‘Input Data calculation result’,a value of 70 and a color ‘yellow’ to a medium risk ‘Input Datacalculation result’, and a value of 10 and a color ‘green’ to a low risk‘Input Data calculation result’, the ‘Risk Assessment Algorithms’ 24will now calculate and establish and generate the above referenced ‘RiskCategories (2) through (8)’ in response to the plurality of RiskValues/plurality of Individual Risks received from the ‘Risk AssessmentLogical Expressions’ 22 in the following manner:

Risk Calculation #2—Average Individual Risk:

The average of all of the ‘Risk Values’ is calculated as follows:

${{Average}\mspace{14mu}{individual}\mspace{14mu}{risk}} = \frac{\sum\limits_{i}^{n}\;{Riskvalue}_{i}}{n}$

In order to determine the ‘Average Individual Risk’, sum the abovereferenced ‘Risk Values’ and then divide by the number of such ‘RiskValues’, where i=number of sample points. The value for the ‘AverageIndividual Risk’ is displayed at the bottom of the colored individualrisk track.

Risk Calculation #3—Risk Subcategory

Referring to the ‘Risk Assessment Output Data’ 18 b 1 set forth above,the following ‘Subcategory Risks’ are defined: (a) gains, (b) losses,(c) stuck and (d) mechanical, where a ‘Subcategory Risk’ (or ‘RiskSubcategory’) is defined as follows:

${{Risk}\mspace{14mu}{Subcategory}} = \frac{\sum\limits_{j}^{n}\left( \;{{Riskvalue}_{j} \times {severity}_{j} \times N_{j}} \right)}{\sum\limits_{j}\left( {{severity}_{j} \times N_{j}} \right)}$

-   -   j=number of individual risks,    -   0≦Severity≦5, and    -   N_(j)=either 1 or 0 depending on whether the Risk Value_(j)        contributes to the sub category    -   Severity j=from the risk matrix catalog.    -   Red risk display for Risk Subcategory≧40    -   Yellow risk display for 20≦Risk Subcategory<40    -   Green risk display for Risk Subcategory<20    -   Risk Calculation #4—Average subcategory risk:

${{Average}\mspace{14mu}{subcategory}\mspace{14mu}{risk}} = \frac{\sum\limits_{i}^{n}\left( \;{{Risk}\mspace{14mu}{Subcategory}_{i} \times {risk}\mspace{14mu}{multiplier}_{i}} \right)}{\underset{1}{\sum\limits^{n}}{{risk}\mspace{14mu}{multiplier}_{i}}}$

-   -   n=number of sample points.

The value for the average subcategory risk is displayed at the bottom ofthe colored subcategory risk track.

-   -   Risk Multiplier=3 for Risk Subcategory≧40,    -   Risk Multiplier=2 for 20≦Risk Subcategory<40    -   Risk Multiplier=1 for Risk Subcategory<20        Risk Calculation #5—Total Risk

The total risk calculation is based on the following categories: (a)gains, (b) losses, (c) stuck, and (d) mechanical.

$\begin{matrix}{{{Risk}\mspace{14mu}{Total}} = {\frac{\sum\limits_{1}^{4}\;{{Risk}\mspace{14mu}{subcategory}_{k}}}{4}\mspace{20mu}{where}}} \\{k = {{number}\mspace{14mu}{of}\mspace{14mu}{subcategories}}}\end{matrix}$

-   -   Red risk display for Risk total≧40    -   Yellow risk display for 20≦Risk Total<40    -   Green risk display for Risk Total<20        Risk Calculation #6—Average Total Risk

${{Average}\mspace{14mu}{total}\mspace{14mu}{risk}} = \frac{\sum\limits_{i}^{n}\left( {{Risk}\mspace{20mu}{Subcategory}_{i} \times {risk}\mspace{14mu}{multiplier}_{i}} \right)}{\underset{1}{\sum\limits^{n}}{{risk}\mspace{14mu}{multiplier}_{i}}}$

-   -   n=number of sample points.    -   Risk Multiplier=3 for Risk Subcategory≧40,    -   Risk Multiplier=2 for 20≦Risk Subcategory<40    -   Risk Multiplier=1 for Risk Subcategory<20

The value for the average total risk is displayed at the bottom of thecolored total risk track.

Risk Calculation #7—Risks per Design Task:

The following 14 design tasks have been defined: Scenario, Trajectory,Mechanical Earth Model, Rig, Wellbore stability, Mud weight and casingpoints, Wellbore Sizes, Casing, Cement, Mud, Bit, Drillstring,Hydraulics, and Time design. There are currently 54 individual risksspecified.

Risk Calculation #7A—Potential Maximum Risk per Design Task

${{Potential}\mspace{14mu}{Risk}_{k}} = \frac{\sum\limits_{j = 1}^{55}\left( {90 \times {Severity}_{k,j} \times N_{k,j}} \right)}{\sum\limits_{j = 1}^{55}\left( {{Severity}_{k,j} \times N_{k,j}} \right)}$

-   -   k=index of design tasks, there are 14 design tasks,    -   N_(j)=either 0 or 1 depending on whether the Risk Value_(j)        contributes to the design task.    -   0≦Severity≦5        Risk Calculation #7B—Actual Risk per Design Task

${{Actual}\mspace{14mu}{Risk}_{k}} = \frac{\sum\limits_{j = 1}^{55}\left( {{Average}\mspace{14mu}{Individual}\mspace{14mu}{Risk}_{j} \times {Severity}_{,j} \times N_{k,j}} \right)}{\sum\limits_{j = 1}^{55}\left( {{Severity}_{j} \times N_{k,j}} \right)}$

-   -   k=index of design tasks, there are 14 design tasks    -   N_(k,j) ε [0, . . . , M]    -   0≦Severity_(j)

-   ≦5

The ‘Severity’ in the above equations are defined as follows:

Risk Severity H2S_CO2 2.67 Hydrates 3.33 Well_WD 3.67 DLS 3 TORT 3Well_MD 4.33 INC 3 Hor_Disp 4.67 DDI 4.33 PP_High 4.33 PP_Low 2.67RockHard 2 RockSoft 1.33 TempHigh 3 Rig_WD 5 Rig_MD 5 SS_BOP 3.67MW_Kick 4 MW_Loss 3 MW_Frac 3.33 MWW 3.33 WBS 3 WBSW 3.33 HSLength 3Hole_Big 2 Hole_Sm 2.67 Hole_Csg 2.67 Csg_Csg 2.33 Csg_Bit 1.67 Csg_DF 4Csg_Wt 3 Csg_MOP 2.67 Csg_Wear 1.33 Csg_Count 4.33 TOC_Low 1.67 Cmt_Kick3.33 Cmt_Loss 2.33 Cmt_Frac 3.33 Bit_Wk 2.33 Bit_WkXS 2.33 Bit_Ftg 2.33Bit_Hrs 2 Bit_Krev 2 Bit_ROP 2 Bit_UCS 3 DS_MOP 3.67 DS_Part 3 Kick_Tol4.33 Q_Crit 2.67 Q_Max 3.33 Cutting 3.33 P_Max 4 TFA_Low 1.33 ECD_Frac 4ECD_Loss 3.33

Refer now to FIG. 11 which will be used during the following functionaldescription of the operation of the present invention.

A functional description of the operation of the ‘Automatic WellPlanning Risk Assessment Software’ 18 c 1 will be set forth in thefollowing paragraphs with reference to FIGS. 1 through 11 of thedrawings.

The Input Data 20 a shown in FIG. 9A will be introduced as ‘input data’to the Computer System 18 of FIG. 9A. The Processor 18 a will executethe Automatic Well Planning Risk Assessment Software 18 c 1, while usingthe Input Data 20 a, and, responsive thereto, the Processor 18 a willgenerate the Risk Assessment Output Data 18 b 1, the Risk AssessmentOutput Data 18 b 1 being recorded or displayed on the Recorder orDisplay Device 18 b in the manner illustrated in FIG. 9B. The RiskAssessment Output Data 18 b 1 includes the ‘Risk Categories’, the‘Subcategory Risks’, and the ‘Individual Risks’. When the Automatic WellPlanning Risk Assessment Software 18 c 1 is executed by the Processor 18a of FIG. 9A, referring to FIGS. 10 and 11, the Input Data 20 a (and theRisk Assessment Constants 26 and the Risk Assessment Catalogs 28) arecollectively provided as ‘input data’ to the Risk Assessment LogicalExpressions 22. Recall that the Input Data 20 a includes a ‘plurality ofInput Data Calculation results’. As a result, as denoted by elementnumeral 32 in FIG. 11, the ‘plurality of Input Data Calculation results’associated with the Input Data 20 a will be provided directly to theLogical Expressions block 22 in FIG. 11. During that execution of theLogical Expressions 22 by the Processor 18 a, each of the ‘plurality ofInput Data Calculation results’ from the Input Data 20 a will becompared with each of the ‘logical expressions’ in the Risk AssessmentLogical Expressions block 22 in FIG. 11. When a match is found betweenan ‘Input Data Calculation result’ from the Input Data 20 a and an‘expression’ in the Logical Expressions block 22, a ‘Risk Value’ or‘Individual Risk’ 34 will be generated (by the Processor 18 a) from theLogical Expressions block 22 in FIG. 11. As a result, since a ‘pluralityof Input Data Calculation results’ 32 from the Input Data 20 a have beencompared with a ‘plurality of expressions’ in the Logical Expressions’block 22 in FIG. 11, the Logical Expressions block 22 will generate aplurality of Risk Values/plurality of Individual Risks 34 in FIG. 11,where each of the plurality of Risk Values/plurality of Individual Riskson line 34 in FIG. 11 that are generated by the Logical Expressionsblock 22 will represent an ‘Input Data Calculation result’ from theInput Data 20 a that has been ranked as either a ‘High Risk’, or a‘Medium Risk’, or a ‘Low Risk’ by the Logical Expressions block 22.Therefore, a ‘Risk Value’ or ‘Individual Risk’ is defined as an ‘InputData Calculation result’ from the Input Data 20 a that has been matchedwith one of the ‘expressions’ in the Logical Expressions 22 and ranked,by the Logical Expressions block 22, as either a ‘High Risk’, or a‘Medium Risk’, or a ‘Low Risk’. For example, consider the following‘expression’ in the Logical Expressions’ 22:

-   Task: MudWindow-   Description: Hole section length (per hole section)-   Short Name: HSLength-   Data Name:-   Calculation: =HoleEnd−HoleStart-   Calculation Method: CalculateHSLength-   High: >=8000-   Medium: >=7001-   Low: <7001

The ‘Hole End−HoleStart’ calculation is an ‘Input Data Calculationresult’ from the Input Data 20 a. The Processor 18 a will find a matchbetween the ‘Hole End−HoleStart Input Data Calculation result’originating from the Input Data 20 a and the above identified‘expression’ in the Logical Expressions 22. As a result, the LogicalExpressions block 22 will ‘rank’ the ‘Hole End−HoleStart Input DataCalculation result’ as either a ‘High Risk’, or a ‘Medium Risk’, or a‘Low Risk’ depending upon the value of the ‘Hole End−HoleStart InputData Calculation result’.

When the ‘Risk Assessment Logical Expressions’ 22 ranks the ‘Input Datacalculation result’ as either a ‘high risk’ or a ‘medium risk’ or a ‘lowrisk’ thereby generating a plurality of ranked Risk Values/plurality ofranked Individual Risks, the ‘Risk Assessment Logical Algorithms’ 24will then assign a ’value’ and a ‘color’ to that ranked ‘Risk Value’ orranked ‘Individual Risk’, where the ‘value’ and the ‘color’ depends uponthe particular ranking (i.e., the ‘high risk’ rank, or the ‘medium risk’rank, or the ‘low risk’ rank) that is associated with that ‘Risk Value’or ‘Individual Risk’. The ‘value’ and the ‘color’ is assigned, by the‘Risk Assessment Logical Algorithms’ 24, to the ranked ‘Risk Values’ orranked ‘Individual Risks’ in the following manner:

-   a High risk=90,-   a Medium risk=70, and-   a Low risk=10-   High risk color code=Red-   Medium risk color code=Yellow-   Low risk color code=Green

If the ‘Risk Assessment Logical Expressions’ 22 assigns a ‘high risk’rank to the ‘Input Data calculation result’ thereby generating a ranked‘Individual Risk’, the ‘Risk Assessment Logical Algorithms’ 24 assigns avalue ‘90’ to that ranked ‘Risk Value’ or ranked ‘Individual Risk’ and acolor ‘red’ to that ranked ‘Risk Value’ or that ranked ‘IndividualRisk’. If the ‘Risk Assessment Logical Expressions’ 22 assigns a ‘mediumrisk’ rank to the ‘Input Data calculation result’ thereby generating aranked ‘Individual Risk’, the ‘Risk Assessment Logical Algorithms’ 24assigns a value ‘70’ to that ranked ‘Risk Value’ or ranked ‘IndividualRisk’ and a color ‘yellow’ to that ranked ‘Risk Value’ or that ranked‘Individual Risk’. If the ‘Risk Assessment Logical Expressions’ 22assigns a ‘low risk’ rank to the ‘Input Data calculation result’ therebygenerating a ranked ‘Individual Risk’, the ‘Risk Assessment LogicalAlgorithms’ 24 assigns a value ‘10’ to that ranked ‘Risk Value’ orranked ‘Individual Risk’ and a color ‘green’ to that ranked ‘Risk Value’or that ranked ‘Individual Risk’.

Therefore, in FIG. 11, a plurality of ranked Individual Risks (or rankedRisk Values) is generated, along line 34, by the Logical Expressionsblock 22, the plurality of ranked Individual Risks (which forms a partof the ‘Risk Assessment Output Data’ 18 b 1) being provided directly tothe ‘Risk Assessment Algorithms’ block 24. The ‘Risk AssessmentAlgorithms’ block 24 will receive the plurality of ranked IndividualRisks’ from line 34 and, responsive thereto, the ‘Risk AssessmentAlgorithms’ 24 will: (1) generate the ‘Ranked Individual Risks’including the ‘values’ and ‘colors’ associated therewith in the mannerdescribed above, and, in addition, (2) calculate and generate the‘Ranked Risk Categories’ 40 and the ‘Ranked Subcategory Risks’ 40associated with the ‘Risk Assessment Output Data’ 18 b 1. The ‘RankedRisk Categories’ 40 and the ‘Ranked Subcategory Risks’ 40 and the‘Ranked Individual Risks’ 40 can now be recorded or displayed on theRecorder or Display device 18 b. Recall that the ‘Ranked RiskCategories’ 40 include: an Average Individual Risk, an AverageSubcategory Risk, a Risk Total (or Total Risk), an Average Total Risk, apotential Risk for each design task, and an Actual Risk for each designtask. Recall that the ‘Ranked Subcategory Risks’ 40 include: a RiskSubcategory (or Subcategory Risk).

As a result, recalling that the ‘Risk Assessment Output Data’ 18 b 1includes ‘one or more Risk Categories’ and ‘one or more SubcategoryRisks’ and ‘one or more Individual Risks’, the ‘Risk Assessment OutputData’ 18 b 1, which includes the Risk Categories 40 and the SubcategoryRisks 40 and the Individual Risks 40, can now be recorded or displayedon the Recorder or Display Device 18 b of the Computer System 18 shownin FIG. 9A.

As noted earlier, the ‘Risk Assessment Algorithms’ 24 will receive the‘Ranked Individual Risks’ from the Logical Expressions 22 along line 34in FIG. 11; and, responsive thereto, the ‘Risk Assessment Algorithms’ 24will (1) assign the ‘values’ and the ‘colors’ to the ‘Ranked IndividualRisks’ in the manner described above, and, in addition, (2) calculateand generate the ‘one or more Risk Categories’ 40 and the ‘one or moreSubcategory Risks’ 40 by using the following equations (set forthabove).

The average Individual Risk is calculated from the ‘Risk Values’ asfollows:

${{Average}\mspace{14mu}{individual}\mspace{14mu}{risk}} = \frac{\sum\limits_{i}^{n}\;{Riskvalue}_{i}}{n}$

The Subcategory Risk, or Risk Subcategory, is calculated from the ‘RiskValues’ and the ‘Severity’, as defined above, as follows:

${{Risk}\mspace{14mu}{Subcategory}} = \frac{\sum\limits_{j}^{n}\left( \;{{Riskvalue}_{j} \times {severity}_{j} \times N_{j}} \right)}{\sum\limits_{j}\left( {{severity}_{j} \times N_{j}} \right)}$

The Average Subcategory Risk is calculated from the Risk Subcategory inthe following manner, as follows:

${{Average}\mspace{14mu}{subcategory}\mspace{14mu}{risk}} = \frac{\sum\limits_{i}^{n}\left( \;{{Risk}\mspace{14mu}{Subcategory}_{i} \times {risk}\mspace{14mu}{multiplier}_{i}} \right)}{\underset{1}{\sum\limits^{n}}{{risk}\mspace{14mu}{multiplier}_{i}}}$

The Risk Total is calculated from the Risk Subcategory in the followingmanner, as follows:

${{Risk}\mspace{14mu}{Total}} = \frac{\sum\limits_{1}^{4}{{Risk}\mspace{14mu}{subcategory}_{k}}}{4}$

The Average Total Risk is calculated from the Risk Subcategory in thefollowing manner, as follows:

${{Average}\mspace{14mu}{total}\mspace{14mu}{risk}} = \frac{\sum\limits_{i}^{n}\left( {{Risk}\mspace{14mu}{Subcategory}_{i} \times {risk}\mspace{14mu}{multiplier}_{i}} \right)}{\sum\limits_{1}^{n}{{risk}\mspace{14mu}{multiplier}_{i}}}$

The Potential Risk is calculated from the Severity, as defined above, asfollow:

${{Potential}\mspace{14mu}{Risk}_{k}} = \frac{\sum\limits_{j = 1}^{55}\left( {90 \times {Severity}_{k,j} \times N_{k,j}} \right)}{\sum\limits_{j = 1}^{55}\left( {{Severity}_{k,j} \times N_{k,j}} \right)}$

The Actual Risk is calculated from the Average Individual Risk and theSeverity (defined above) as follows:

${{Actual}\mspace{14mu}{Risk}_{k}} = \frac{\sum\limits_{j = 1}^{55}\left( {{Average}\mspace{14mu}{Individual}\mspace{14mu}{Risk}_{j} \times {Severity}_{,j} \times N_{k,j}} \right)}{\sum\limits_{j = 1}^{55}\left( {{Severity}_{j} \times N_{k,j}} \right)}$

Recall that the Logical Expressions block 22 will generate a ‘pluralityof Risk Values/Ranked Individual Risks’ along line 34 in FIG. 11, whereeach of the ‘plurality of Risk Values/Ranked Individual Risks’ generatedalong line 34 represents a received ‘Input Data Calculation result’ fromthe Input Data 20 a that has been ‘ranked’ as either a ‘High Risk’, or a‘Medium Risk’, or a ‘Low Risk’ by the Logical Expressions 22. A ‘HighRisk’ will be assigned a ‘Red’ color, and a ‘Medium Risk’ will beassigned a ‘Yellow’ color, and a ‘Low Risk’ will be assigned a ‘Green’color. Therefore, noting the word ‘rank’ in the following, the LogicalExpressions block 22 will generate (along line 34 in FIG. 11) a‘plurality of ranked Risk Values/ranked Individual Risks’.

In addition, in FIG. 11, recall that the ‘Risk Assessment Algorithms’block 24 will receive (from line 34) the ‘plurality of ranked RiskValues/ranked Individual Risks’ from the Logical Expressions block 22.In response thereto, noting the word ‘rank’ in the following, the ‘RiskAssessment Algorithms’ block 24 will generate: (1) the ‘one or moreIndividual Risks having ‘values’ and ‘colors’ assigned thereto, (2) the‘one or more ranked Risk Categories’ 40, and (3) the ‘one or more rankedSubcategory Risks’ 40. Since the ‘Risk Categories’ and the ‘SubcategoryRisks’ are each ‘ranked’, a ‘High Risk’ (associated with a Risk Category40 or a Subcategory Risk 40) will be assigned a ‘Red’ color, and a‘Medium Risk’ will be assigned a ‘Yellow’ color, and a ‘Low Risk’ willbe assigned a ‘Green’ color. In view of the above ‘rankings’ and thecolors associated therewith, the ‘Risk Assessment Output Data’ 18 b 1,including the ‘ranked’ Risk Categories 40 and the ‘ranked’ SubcategoryRisks 40 and the ‘ranked’ Individual Risks 38, will be recorded ordisplayed on the Recorder or Display Device 18 b of the Computer System18 shown in FIG. 9A in the manner illustrated in FIG. 9B.

The invention being thus described, it will be obvious that the same maybe varied in many ways. Such variations are not to be regarded as adeparture from the spirit and scope of the invention, and all suchmodifications as would be obvious to one skilled in the art are intendedto be included within the scope of the following claims.

1. A program storage device readable by a machine tangibly embodying aprogram of instructions executable by the machine to perform methodsteps for determining and displaying risk information based on atechnical wellbore design and Earth properties, the method stepscomprising: generating a drillstring design for a wellbore in each holesection of the wellbore in response to a required wellbore geometry anda required wellbore trajectory of the wellbore; receiving input data,the input data including input data calculation results associated withthe wellbore; comparing each calculation result of the input datacalculation results with a logical expression; ranking by the logicalexpression each of the input data calculation results, and generatingranked risk values extending along a depth of the wellbore in responsethereto, each of the ranked risk values representing an input datacalculation result that has been ranked by the logical expression ashaving a risk selected from the group of a high risk severity, a mediumrisk severity, and a low risk severity; generating risk information inresponse to the ranked risk values, the risk information comprising aranked risk category, a ranked subcategory risk and a plurality ofranked individual risks; and displaying the risk information, said riskinformation display including a simultaneous display of said riskinformation along said depth of said wellbore.
 2. The program storagedevice of claim 1, wherein the risk category is selected from a groupconsisting of: an average individual risk, an average subcategory risk,a total risk, an average total risk, a potential risk for a design task,and an actual risk for the design task.
 3. The program storage device ofclaim 1, wherein the subcategory risk is selected from a groupconsisting of: gains risks, losses risks, stuck pipe risks, andmechanical risks.
 4. The program storage device of claim 1, wherein theindividual risks are selected from a group consisting of: H2S and CO2,hydrates, well water depth, tortuosity, dogleg severity, directionaldrilling index, inclination, horizontal displacement, casing wear, highpore pressure, low pore pressure, hard rock, soft rock, hightemperature, water-depth to rig rating, well depth to rig rating, mudweight to kick, mud weight to losses, mud weight to fracture, mud weightwindow, wellbore stability window, wellbore stability, hole sectionlength, casing design factor, hole to casing clearance, casing to casingclearance, casing to bit clearance, casing linear weight, casing maximumoverpull, low top of cement, cement to kick, cement to losses, cement tofracture, bit excess work, bit work, bit footage, bit hours, bitrevolutions, bit rate of penetration, drillstring maximum overpull, bitcompressive strength, kick tolerance, critical flow rate, maximum flowrate, small nozzle area, standpipe pressure, ECD to fracture, ECD tolosses, gains, gains average, losses, losses average, stuck, stuckaverage, mechanical, mechanical average, risk average, subsea BOP, largehole, small hole, number of casing strings, drillstring parting, andcuttings.
 5. The program storage device of claim 1, wherein the step ofgenerating the risk information in response to the ranked risk valuescomprises the step of receiving the ranked risk values and calculatingthe ranked risk categories.
 6. The program storage device of claim 1,wherein the step of generating the risk information in response to theranked risk values comprises the step of receiving ranked risk valuesand calculating the subcategory risks.
 7. The program storage device ofclaim 1, wherein the step of generating the risk information in responseto the ranked risk values comprises the steps of receiving the rankedrisk values and using the ranked risk values to represent the rankedindividual risks.
 8. A program storage device readable by a machinetangibly embodying a program of instructions executable by the machineto perform method steps for determining and displaying risk informationbased on a technical wellbore design and Earth properties, the methodsteps comprising: receiving a plurality of input data calculationresults associated with the wellbore; comparing each calculation resultof the plurality of input data calculation results with each logicalexpression of a plurality of logical expressions to rank the calculationresult; calculating a plurality of ranked individual risks extendingalong a depth of the wellbore in response to the ranking step, each ofthe plurality of ranked individual risks representing an input datacalculation result that has been ranked by the logical expression ashaving a risk severity selected from the group consisting of a high riskseverity, a medium risk severity, and a low risk severity; generatingrisk information in response to the plurality of ranked individual risk;and displaying the risk information, the displaying step includingdisplaying the risk information on a risk information display, the riskinformation display including a simultaneous display of the plurality ofranked individual risks calculated along the depth of the wellbore.